Crea un clúster de Dataproc con bibliotecas cliente

El siguiente código de muestra indica cómo usar las bibliotecas cliente de Cloud para crear un clúster de Dataproc, ejecutar un trabajo en el clúster y, luego, borrar el clúster.

También puedes realizar estas tareas con las siguientes herramientas:

Antes de comenzar

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  3. Make sure that billing is enabled for your Google Cloud project.

  4. Enable the Dataproc API.

    Enable the API

    5.

  5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  6. Make sure that billing is enabled for your Google Cloud project.

  7. Enable the Dataproc API.

    Enable the API

    8.

Ejecuta el código

Prueba la explicación: Haz clic en Open in Cloud Shell para ejecutar una explicación de las bibliotecas cliente de Cloud de Python que crea un clúster, ejecuta un trabajo de PySpark y, luego, borra el clúster.

Abrir en Cloud Shell

Go

  1. Instala la biblioteca cliente Para obtener más información, consulta la documentación sobre cómo configurar tu entorno de desarrollo.
  2. Configura la autenticación
  3. Clona y ejecuta el código de muestra de GitHub.
  4. Revisa el resultado. El código muestra el registro del controlador del trabajo en el bucket de staging predeterminado de Dataproc en Cloud Storage. Puedes ver el resultado del controlador del trabajo desde la consola de Google Cloud en la sección Trabajos de Dataproc de tu proyecto. Haz clic en el ID de tarea para ver el resultado del trabajo en la página Detalles del trabajo.


// This quickstart shows how you can use the Dataproc Client library to create a
// Dataproc cluster, submit a PySpark job to the cluster, wait for the job to finish
// and finally delete the cluster.
//
// Usage:
//
//	go build
//	./quickstart --project_id <PROJECT_ID> --region <REGION> \
//	    --cluster_name <CLUSTER_NAME> --job_file_path <GCS_JOB_FILE_PATH>
package main

import (
	"context"
	"flag"
	"fmt"
	"io"
	"log"
	"regexp"

	dataproc "cloud.google.com/go/dataproc/apiv1"
	"cloud.google.com/go/dataproc/apiv1/dataprocpb"
	"cloud.google.com/go/storage"
	"google.golang.org/api/option"
)

func main() {
	var projectID, clusterName, region, jobFilePath string
	flag.StringVar(&projectID, "project_id", "", "Cloud Project ID, used for creating resources.")
	flag.StringVar(&region, "region", "", "Region that resources should be created in.")
	flag.StringVar(&clusterName, "cluster_name", "", "Name of Cloud Dataproc cluster to create.")
	flag.StringVar(&jobFilePath, "job_file_path", "", "Path to job file in GCS.")
	flag.Parse()

	ctx := context.Background()

	// Create the cluster client.
	endpoint := fmt.Sprintf("%s-dataproc.googleapis.com:443", region)
	clusterClient, err := dataproc.NewClusterControllerClient(ctx, option.WithEndpoint(endpoint))
	if err != nil {
		log.Fatalf("error creating the cluster client: %s\n", err)
	}

	// Create the cluster config.
	createReq := &dataprocpb.CreateClusterRequest{
		ProjectId: projectID,
		Region:    region,
		Cluster: &dataprocpb.Cluster{
			ProjectId:   projectID,
			ClusterName: clusterName,
			Config: &dataprocpb.ClusterConfig{
				MasterConfig: &dataprocpb.InstanceGroupConfig{
					NumInstances:   1,
					MachineTypeUri: "n1-standard-2",
				},
				WorkerConfig: &dataprocpb.InstanceGroupConfig{
					NumInstances:   2,
					MachineTypeUri: "n1-standard-2",
				},
			},
		},
	}

	// Create the cluster.
	createOp, err := clusterClient.CreateCluster(ctx, createReq)
	if err != nil {
		log.Fatalf("error submitting the cluster creation request: %v\n", err)
	}

	createResp, err := createOp.Wait(ctx)
	if err != nil {
		log.Fatalf("error creating the cluster: %v\n", err)
	}

	// Defer cluster deletion.
	defer func() {
		dReq := &dataprocpb.DeleteClusterRequest{
			ProjectId:   projectID,
			Region:      region,
			ClusterName: clusterName,
		}
		deleteOp, err := clusterClient.DeleteCluster(ctx, dReq)
		deleteOp.Wait(ctx)
		if err != nil {
			fmt.Printf("error deleting cluster %q: %v\n", clusterName, err)
			return
		}
		fmt.Printf("Cluster %q successfully deleted\n", clusterName)
	}()

	// Output a success message.
	fmt.Printf("Cluster created successfully: %q\n", createResp.ClusterName)

	// Create the job client.
	jobClient, err := dataproc.NewJobControllerClient(ctx, option.WithEndpoint(endpoint))

	// Create the job config.
	submitJobReq := &dataprocpb.SubmitJobRequest{
		ProjectId: projectID,
		Region:    region,
		Job: &dataprocpb.Job{
			Placement: &dataprocpb.JobPlacement{
				ClusterName: clusterName,
			},
			TypeJob: &dataprocpb.Job_PysparkJob{
				PysparkJob: &dataprocpb.PySparkJob{
					MainPythonFileUri: jobFilePath,
				},
			},
		},
	}

	submitJobOp, err := jobClient.SubmitJobAsOperation(ctx, submitJobReq)
	if err != nil {
		fmt.Printf("error with request to submitting job: %v\n", err)
		return
	}

	submitJobResp, err := submitJobOp.Wait(ctx)
	if err != nil {
		fmt.Printf("error submitting job: %v\n", err)
		return
	}

	re := regexp.MustCompile("gs://(.+?)/(.+)")
	matches := re.FindStringSubmatch(submitJobResp.DriverOutputResourceUri)

	if len(matches) < 3 {
		fmt.Printf("regex error: %s\n", submitJobResp.DriverOutputResourceUri)
		return
	}

	// Dataproc job outget gets saved to a GCS bucket allocated to it.
	storageClient, err := storage.NewClient(ctx)
	if err != nil {
		fmt.Printf("error creating storage client: %v\n", err)
		return
	}

	obj := fmt.Sprintf("%s.000000000", matches[2])
	reader, err := storageClient.Bucket(matches[1]).Object(obj).NewReader(ctx)
	if err != nil {
		fmt.Printf("error reading job output: %v\n", err)
		return
	}

	defer reader.Close()

	body, err := io.ReadAll(reader)
	if err != nil {
		fmt.Printf("could not read output from Dataproc Job: %v\n", err)
		return
	}

	fmt.Printf("Job finished successfully: %s", body)
}

Java

  1. Instala la biblioteca cliente Para obtener más información, consulta la documentación sobre cómo configurar un entorno de desarrollo Java.
  2. Configura la autenticación
  3. Clona y ejecuta el código de muestra de GitHub.
  4. Revisa el resultado. El código muestra el registro del controlador del trabajo en el bucket de staging predeterminado de Dataproc en Cloud Storage. Puedes ver el resultado del controlador del trabajo desde la consola de Google Cloud en la sección Trabajos de Dataproc de tu proyecto. Haz clic en el ID de tarea para ver el resultado del trabajo en la página Detalles del trabajo.

/* This quickstart sample walks a user through creating a Cloud Dataproc
 * cluster, submitting a PySpark job from Google Cloud Storage to the
 * cluster, reading the output of the job and deleting the cluster, all
 * using the Java client library.
 *
 * Usage:
 *     mvn clean package -DskipTests
 *
 *     mvn exec:java -Dexec.args="<PROJECT_ID> <REGION> <CLUSTER_NAME> <GCS_JOB_FILE_PATH>"
 *
 *     You can also set these arguments in the main function instead of providing them via the CLI.
 */

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.dataproc.v1.Cluster;
import com.google.cloud.dataproc.v1.ClusterConfig;
import com.google.cloud.dataproc.v1.ClusterControllerClient;
import com.google.cloud.dataproc.v1.ClusterControllerSettings;
import com.google.cloud.dataproc.v1.ClusterOperationMetadata;
import com.google.cloud.dataproc.v1.InstanceGroupConfig;
import com.google.cloud.dataproc.v1.Job;
import com.google.cloud.dataproc.v1.JobControllerClient;
import com.google.cloud.dataproc.v1.JobControllerSettings;
import com.google.cloud.dataproc.v1.JobMetadata;
import com.google.cloud.dataproc.v1.JobPlacement;
import com.google.cloud.dataproc.v1.PySparkJob;
import com.google.cloud.storage.Blob;
import com.google.cloud.storage.Storage;
import com.google.cloud.storage.StorageOptions;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.regex.Matcher;
import java.util.regex.Pattern;

public class Quickstart {

  public static void quickstart(
      String projectId, String region, String clusterName, String jobFilePath)
      throws IOException, InterruptedException {
    String myEndpoint = String.format("%s-dataproc.googleapis.com:443", region);

    // Configure the settings for the cluster controller client.
    ClusterControllerSettings clusterControllerSettings =
        ClusterControllerSettings.newBuilder().setEndpoint(myEndpoint).build();

    // Configure the settings for the job controller client.
    JobControllerSettings jobControllerSettings =
        JobControllerSettings.newBuilder().setEndpoint(myEndpoint).build();

    // Create both a cluster controller client and job controller client with the
    // configured settings. The client only needs to be created once and can be reused for
    // multiple requests. Using a try-with-resources closes the client, but this can also be done
    // manually with the .close() method.
    try (ClusterControllerClient clusterControllerClient =
            ClusterControllerClient.create(clusterControllerSettings);
        JobControllerClient jobControllerClient =
            JobControllerClient.create(jobControllerSettings)) {
      // Configure the settings for our cluster.
      InstanceGroupConfig masterConfig =
          InstanceGroupConfig.newBuilder()
              .setMachineTypeUri("n1-standard-2")
              .setNumInstances(1)
              .build();
      InstanceGroupConfig workerConfig =
          InstanceGroupConfig.newBuilder()
              .setMachineTypeUri("n1-standard-2")
              .setNumInstances(2)
              .build();
      ClusterConfig clusterConfig =
          ClusterConfig.newBuilder()
              .setMasterConfig(masterConfig)
              .setWorkerConfig(workerConfig)
              .build();
      // Create the cluster object with the desired cluster config.
      Cluster cluster =
          Cluster.newBuilder().setClusterName(clusterName).setConfig(clusterConfig).build();

      // Create the Cloud Dataproc cluster.
      OperationFuture<Cluster, ClusterOperationMetadata> createClusterAsyncRequest =
          clusterControllerClient.createClusterAsync(projectId, region, cluster);
      Cluster clusterResponse = createClusterAsyncRequest.get();
      System.out.println(
          String.format("Cluster created successfully: %s", clusterResponse.getClusterName()));

      // Configure the settings for our job.
      JobPlacement jobPlacement = JobPlacement.newBuilder().setClusterName(clusterName).build();
      PySparkJob pySparkJob = PySparkJob.newBuilder().setMainPythonFileUri(jobFilePath).build();
      Job job = Job.newBuilder().setPlacement(jobPlacement).setPysparkJob(pySparkJob).build();

      // Submit an asynchronous request to execute the job.
      OperationFuture<Job, JobMetadata> submitJobAsOperationAsyncRequest =
          jobControllerClient.submitJobAsOperationAsync(projectId, region, job);
      Job jobResponse = submitJobAsOperationAsyncRequest.get();

      // Print output from Google Cloud Storage.
      Matcher matches =
          Pattern.compile("gs://(.*?)/(.*)").matcher(jobResponse.getDriverOutputResourceUri());
      matches.matches();

      Storage storage = StorageOptions.getDefaultInstance().getService();
      Blob blob = storage.get(matches.group(1), String.format("%s.000000000", matches.group(2)));

      System.out.println(
          String.format("Job finished successfully: %s", new String(blob.getContent())));

      // Delete the cluster.
      OperationFuture<Empty, ClusterOperationMetadata> deleteClusterAsyncRequest =
          clusterControllerClient.deleteClusterAsync(projectId, region, clusterName);
      deleteClusterAsyncRequest.get();
      System.out.println(String.format("Cluster \"%s\" successfully deleted.", clusterName));

    } catch (ExecutionException e) {
      System.err.println(String.format("quickstart: %s ", e.getMessage()));
    }
  }

  public static void main(String... args) throws IOException, InterruptedException {
    if (args.length != 4) {
      System.err.println(
          "Insufficient number of parameters provided. Please make sure a "
              + "PROJECT_ID, REGION, CLUSTER_NAME and JOB_FILE_PATH are provided, in this order.");
      return;
    }

    String projectId = args[0]; // project-id of project to create the cluster in
    String region = args[1]; // region to create the cluster
    String clusterName = args[2]; // name of the cluster
    String jobFilePath = args[3]; // location in GCS of the PySpark job

    quickstart(projectId, region, clusterName, jobFilePath);
  }
}

Node.js

  1. Instala la biblioteca cliente Para obtener más información, consulta la documentación sobre cómo configurar un entorno de desarrollo de Node.js.
  2. Configura la autenticación
  3. Clona y ejecuta el código de muestra de GitHub.
  4. Revisa el resultado. El código muestra el registro del controlador del trabajo en el bucket de staging predeterminado de Dataproc en Cloud Storage. Puedes ver el resultado del controlador del trabajo desde la consola de Google Cloud en la sección Trabajos de Dataproc de tu proyecto. Haz clic en el ID de tarea para ver el resultado del trabajo en la página Detalles del trabajo.

// This quickstart sample walks a user through creating a Dataproc
// cluster, submitting a PySpark job from Google Cloud Storage to the
// cluster, reading the output of the job and deleting the cluster, all
// using the Node.js client library.

'use strict';

function main(projectId, region, clusterName, jobFilePath) {
  const dataproc = require('@google-cloud/dataproc');
  const {Storage} = require('@google-cloud/storage');

  // Create a cluster client with the endpoint set to the desired cluster region
  const clusterClient = new dataproc.v1.ClusterControllerClient({
    apiEndpoint: `${region}-dataproc.googleapis.com`,
    projectId: projectId,
  });

  // Create a job client with the endpoint set to the desired cluster region
  const jobClient = new dataproc.v1.JobControllerClient({
    apiEndpoint: `${region}-dataproc.googleapis.com`,
    projectId: projectId,
  });

  async function quickstart() {
    // Create the cluster config
    const cluster = {
      projectId: projectId,
      region: region,
      cluster: {
        clusterName: clusterName,
        config: {
          masterConfig: {
            numInstances: 1,
            machineTypeUri: 'n1-standard-2',
          },
          workerConfig: {
            numInstances: 2,
            machineTypeUri: 'n1-standard-2',
          },
        },
      },
    };

    // Create the cluster
    const [operation] = await clusterClient.createCluster(cluster);
    const [response] = await operation.promise();

    // Output a success message
    console.log(`Cluster created successfully: ${response.clusterName}`);

    const job = {
      projectId: projectId,
      region: region,
      job: {
        placement: {
          clusterName: clusterName,
        },
        pysparkJob: {
          mainPythonFileUri: jobFilePath,
        },
      },
    };

    const [jobOperation] = await jobClient.submitJobAsOperation(job);
    const [jobResponse] = await jobOperation.promise();

    const matches =
      jobResponse.driverOutputResourceUri.match('gs://(.*?)/(.*)');

    const storage = new Storage();

    const output = await storage
      .bucket(matches[1])
      .file(`${matches[2]}.000000000`)
      .download();

    // Output a success message.
    console.log(`Job finished successfully: ${output}`);

    // Delete the cluster once the job has terminated.
    const deleteClusterReq = {
      projectId: projectId,
      region: region,
      clusterName: clusterName,
    };

    const [deleteOperation] =
      await clusterClient.deleteCluster(deleteClusterReq);
    await deleteOperation.promise();

    // Output a success message
    console.log(`Cluster ${clusterName} successfully deleted.`);
  }

  quickstart();
}

const args = process.argv.slice(2);

if (args.length !== 4) {
  console.log(
    'Insufficient number of parameters provided. Please make sure a ' +
      'PROJECT_ID, REGION, CLUSTER_NAME and JOB_FILE_PATH are provided, in this order.'
  );
}

main(...args);

Python

  1. Instala la biblioteca cliente Para obtener más información, consulta la documentación sobre cómo configurar un entorno de desarrollo de Python.
  2. Configura la autenticación
  3. Clona y ejecuta el código de muestra de GitHub.
  4. Revisa el resultado. El código muestra el registro del controlador del trabajo en el bucket de staging predeterminado de Dataproc en Cloud Storage. Puedes ver el resultado del controlador del trabajo desde la consola de Google Cloud en la sección Trabajos de Dataproc de tu proyecto. Haz clic en el ID de tarea para ver el resultado del trabajo en la página Detalles del trabajo.

"""
This quickstart sample walks a user through creating a Cloud Dataproc
cluster, submitting a PySpark job from Google Cloud Storage to the
cluster, reading the output of the job and deleting the cluster, all
using the Python client library.

Usage:
    python quickstart.py --project_id <PROJECT_ID> --region <REGION> \
        --cluster_name <CLUSTER_NAME> --job_file_path <GCS_JOB_FILE_PATH>
"""

import argparse
import re

from google.cloud import dataproc_v1 as dataproc
from google.cloud import storage


def quickstart(project_id, region, cluster_name, job_file_path):
    # Create the cluster client.
    cluster_client = dataproc.ClusterControllerClient(
        client_options={"api_endpoint": "{}-dataproc.googleapis.com:443".format(region)}
    )

    # Create the cluster config.
    cluster = {
        "project_id": project_id,
        "cluster_name": cluster_name,
        "config": {
            "master_config": {
                "num_instances": 1,
                "machine_type_uri": "n1-standard-2",
                "disk_config": {"boot_disk_size_gb": 100},
            },
            "worker_config": {
                "num_instances": 2,
                "machine_type_uri": "n1-standard-2",
                "disk_config": {"boot_disk_size_gb": 100},
            },
        },
    }

    # Create the cluster.
    operation = cluster_client.create_cluster(
        request={"project_id": project_id, "region": region, "cluster": cluster}
    )
    result = operation.result()

    print("Cluster created successfully: {}".format(result.cluster_name))

    # Create the job client.
    job_client = dataproc.JobControllerClient(
        client_options={"api_endpoint": "{}-dataproc.googleapis.com:443".format(region)}
    )

    # Create the job config.
    job = {
        "placement": {"cluster_name": cluster_name},
        "pyspark_job": {"main_python_file_uri": job_file_path},
    }

    operation = job_client.submit_job_as_operation(
        request={"project_id": project_id, "region": region, "job": job}
    )
    response = operation.result()

    # Dataproc job output gets saved to the Google Cloud Storage bucket
    # allocated to the job. Use a regex to obtain the bucket and blob info.
    matches = re.match("gs://(.*?)/(.*)", response.driver_output_resource_uri)

    output = (
        storage.Client()
        .get_bucket(matches.group(1))
        .blob(f"{matches.group(2)}.000000000")
        .download_as_bytes()
        .decode("utf-8")
    )

    print(f"Job finished successfully: {output}")

    # Delete the cluster once the job has terminated.
    operation = cluster_client.delete_cluster(
        request={
            "project_id": project_id,
            "region": region,
            "cluster_name": cluster_name,
        }
    )
    operation.result()

    print("Cluster {} successfully deleted.".format(cluster_name))


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description=__doc__,
        formatter_class=argparse.RawDescriptionHelpFormatter,
    )
    parser.add_argument(
        "--project_id",
        type=str,
        required=True,
        help="Project to use for creating resources.",
    )
    parser.add_argument(
        "--region",
        type=str,
        required=True,
        help="Region where the resources should live.",
    )
    parser.add_argument(
        "--cluster_name",
        type=str,
        required=True,
        help="Name to use for creating a cluster.",
    )
    parser.add_argument(
        "--job_file_path",
        type=str,
        required=True,
        help="Job in GCS to execute against the cluster.",
    )

    args = parser.parse_args()
    quickstart(args.project_id, args.region, args.cluster_name, args.job_file_path)

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